9 The high mutational tolerance of CDRs enables optimization of properties necessary for the development of effective antibody‐based therapeutics, including the critical properties of high affinity and specific binding. The CDRs are supported on a β‐sheet framework and can adopt a number of canonical conformations, although CDR3 of the heavy chain exhibits more conformational diversity. This binding flexibility is due to the antibody complementarity determining regions (CDRs), 6 loop regions that are parts of the fragment antigen‐binding (Fab) heavy and light chains. 5, 6, 7, 8 Antibody paratopes-the parts of antibodies that interact with the target antigen-can recognize almost any biomolecular target, with a large range of specificities and affinities. 1, 2, 3, 4 Therapeutic antibodies have certain advantages over small molecules or other protein therapeutics, such as longer serum half‐lives, higher avidity and selectivity, and the ability to invoke desired immune responses. Currently, 46 monoclonal antibodies (mAbs) are marketed for therapeutic use in the United States or Europe, and an increasing number of mAbs are entering late‐stage clinical studies or receiving first approvals. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.Īntibodies (Abs) are an important class of molecules used in research and increasingly as therapeutic agents to treat human diseases. Some methods could also enrich for variants with improved binding affinity FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔ G < −1.0 kcal/mol) in the top 5% of the database. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves the highest AUC values for 527 mutants with |ΔΔ G| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Numerical correlations between computed and observed ΔΔ G values were low ( r = 0.16–0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Using the AB‐Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Our Antibody‐Bind (AB‐Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔ G) across 32 complexes. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. The need for high‐affinity and high‐specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.Īntibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. Some methods could also enrich for variants with improved binding affinity FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |